Trade openness and growth in a small economy: Evidence from Malawi
Bibliographic record
Abstract
Abstract Economists often debate whether a country should pursue a discriminatory regional trade or the broader multilateral trade expansion. This paper contributes to the discourse by presenting empirical evidence on the impacts of regional and multilateral trade openness on economic growth of expansion in Malawi. Using the ARDL‐EC model and data spanning the period 1995–2020, the estimates show that the impact of multilateral trade openness on the long‐run growth rate is positive and statistically significant. At the disaggregated level, the results show that both intra‐ and extra‐African merchandise trade openness have positive and significant impact on the long‐run growth rate. The evidence also suggests that the impact of a change in the level of intra‐African merchandise trade openness on long‐run growth is larger than that of a similar change in the level of extra‐African merchandise trade openness. Thus, our results suggest that policymakers can pursue both regional and multilateral trade expansions simultaneously to accelerate Malawi's economic growth. To augment the benefits of trade, however, we recommend that policymakers focus on deepening intra‐African trade integration and diversifying the country's export base beyond the primary commodities; and to increase the processing of export products to enhance the dynamic benefits to spur faster growth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".